Comparative Analysis of Customer’s Face to Face Interaction using Linear Regression over Random Forest Algorithm with Increase in Accuracy of Prediction

To Perform the Comparative analysis of Customers face to face interaction by comparing Linear regression algorithm and Random forest algorithm. Material and Methods: The pattern size for this innovative telecommunication undertaking was 60 samples (Group 1=30, Group 2=30). For the telecom interaction the available techniques are quick, efficient and more accurate. The main problem in this problem is to derive the data from the different fields which are dependable to predict the accuracy. To analyze using Random forest algorithm technique. It allows you to find the element among the subset that brings to improve a wide variety of ranges to develop the trees. Results: The Random forest set of rules gives higher overall performance with advanced accuracy (97%) than the Linear regression algorithm accuracy (79%). There is no statistical significance between Random forest and linear regression (p=0.767). Conclusion: Random forest algorithm shows better performance based accuracy compared with the Linear regression.